26 pages, 11 figures, 1 table, supplementary information https://doi.org/10.1039/d4fd00160e.-- Data availability: The data in this article are available at https://doi.org/10.3929/ethz-b-000717415, Ice nucleating particles (INPs) catalyze primary ice formation in Arctic low-level mixed-phase clouds, influencing their persistence and radiative properties. Knowledge of the abundance and sources of INP over the remote Arctic Ocean is scarce due to limited data coverage, particularly in the Eurasian Arctic. This study presents summertime measurements of INP concentrations in seawater, fog water and air from the ship-based Arctic Century Expedition, exploring the Barents, Kara, and Laptev Seas, and the adjacent high Arctic islands and archipelagos in August and September 2021. Heat sensitivity tests of ambient aerosols revealed that heat-liable, biogenic INPs make up the majority of Arctic INP populations at temperatures above −20 °C, and to a lesser extent down to −25 °C. INP content in fog water is found to be similar to ambient aerosol, indicating that INP in marine air could also act as cloud condensation nuclei. Measurements of aerosolized INPs using an on-board sea-spray aerosol bubble tank generator exhibit a positive correlation with ambient INP concentrations, but not with INP abundance in seawater samples. INP concentrations in air derived from sea water samples (using a NaCl conversion factor representative for the Arctic) were significantly lower than those measured in ambient air or bubble tank experiments. INP concentrations in bubble tank experiments positively correlated with the phosphate and fluorescence signals in the water. This suggests an important role of the aerosolization mechanism for preferentially partitioning biogenic INPs to the atmosphere, This research used samples and/or data provided by the Arctic Century Expedition, a joint initiative led by the Swiss Polar Institute (SPI), the Antarctic and Arctic Research Institute (AARI), and GEOMAR Helmholtz Centre for Ocean Research Kiel (GEOMAR), and funded by the Swiss Polar Foundation. GL and ZAK acknowledge that this project has been made possible by a grant from the Swiss Polar Institute, Dr Frederik Paulsen. GPF acknowledges funding from the Academy of Finland (Grant No. 342227). ZAK acknowledges this project has received funding from the Horizon Europe program under Grant Agreement No. 101137680 (Project CERTAINTY), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

This research used samples and/or data provided by the Arctic Century Expedition, a joint initiative led by the Swiss Polar Institute (SPI), the Antarctic and Arctic Research Institute (AARI), and GEOMAR Helmholtz Centre for Ocean Research Kiel (GEOMAR), and funded by the Swiss Polar Foundation. GL and ZAK acknowledge that this project has been made possible by a grant from the Swiss Polar Institute, Dr Frederik Paulsen. GPF acknowledges funding from the Academy of Finland (Grant No. 342227). ZAK acknowledges this project has received funding from the Horizon Europe program under Grant Agreement No. 101137680 (Project CERTAINTY), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

60 pages, 7 figures, 1 table, supplemental material https://doi.org/10.1525/elementa.2025.00067.-- Data accessibility statement:
Seawater MeSH and DMS
TRANSSIZ data during spring in the Norwegian Sea and Svalbard archipelago are available at (https://doi.org/10.1594/PANGAEA.953917). Summer Baltic Sea, Skagerrak Sea, and North Sea data from Leck and Rodhe (1991; https://doi.org/10.1007/BF00053934) are available upon request. OC1607A and OC1708A data during summer in the sub-Arctic Northeast Pacific are available at https://doi.org/10.26008/1912/bco-dmo.705636.2. Blanes Bay observatory data covering nearly a full year of measurements in the Mediterranean Catalan Coast are available at https://doi.org/10.5281/zenodo.12758702. AMT-7 data spanning from England to the Falkland Islands during September and October are available at https://doi.org/10.5281/zenodo.12759177. Great Barrier Reef data during summer in the central Great Barrier Reef are available at https://doi.org/10.25918/data.410. POLAR-CHANGE data in the summer/autumn Antarctic Peninsula and Weddell sea are available at https://doi.org/10.5281/zenodo.12758981


Atmospheric MeSH and DMS

WAO data during spring/summer in UK East Coast are available at https://doi.org/10.5281/zenodo.14697890. AGENA data during summer in the Azores are available at https://minds.wisconsin.edu/handle/1793/85493. Scripps Pier data during autumn in South West California, USA, are available at https://minds.wisconsin.edu/handle/1793/82383. Kennaook/Cape Grim data during autumn, winter, spring, and summer, in North West Tasmania, are available at https://doi.org/10.25919/njv7-a719 and https://doi.org/10.25919/we93-1306. SOAP data during the summer/autumn in the Chatham Rise are available at https://doi.org/10.25919/5d914b00c5759. CAPRICORN data during the summer/autumn in the Southern Ocean south of Tasmania are available at https://doi.org/10.25919/574m-7e47. MISO data during the summer/autumn Southern Ocean/Antarctic ice edge are available at https://doi.org/10.25919/p8bz-p724. ARTofMELT data during the spring/summer Fram Strait are available upon request.

Aerosol size distributions and atmospheric chemical compositions

Aerosol size distributions and atmospheric chemical compositions data used to produce Figure 5 are publicly available or extracted from graphs in published papers using an image extraction technique, WebPlotDizitizer (https://automeris.io/). Monthly occurrence probability of aerosol size distribution types at Zeppelin, Villum, and Halley was extracted from figures in DallOsto et al. (2017), Pernov et al. (2022), and Lachlan-Cope et al. (2020), respectively. DMS concentrations at Villum and Halley are available at Pernov et al. (2020) and Read and Lewis (2007), while those at Gruvebadet and Dumont d’Urville were extracted from Jang et al. (2021) and Legrand et al. (2017), respectively. Chemical compositions for total particulate matter (TPM) and ozone concentrations at Alert (2000–2002) and Zeppelin (2000–2015) were downloaded from EBAS data repository (https://ebas.nilu.no/). Chemical compositions for PM1 and ozone concentrations at Utqiaġvik for 2000–2010 are available at NOAA PMEL data repository (https://www.pmel.noaa.gov/acg/data/) for aerosols and NOAA ESRL data repository for ozone (McClure-Begley et al. 2014; http://dx.doi.org/10.7289/V57P8WBF). Non-sea-salt sulfate and MSA for PM10 at Gruvebadet (2010–2017) were extracted from Jang et al. (2021) and Becagli et al. (2019), respectively. Non-sea-salt sulfate and MSA for TPM (1991–1996), sodium for TPM (2004–2013), and ozone concentrations (2004–2014) at Dumont d’Urville were extracted from Legrand et al. (2017), Legrand et al. (2016a), and Legrand et al. (2016b), respectively. Chemical compositions for PM10 (2000–2007) and ozone (2000–2004) at Neumayer are available at PANGAEA (Weller et al., 2011b; https://doi.org/10.1594/PANGAEA.756382) and EBAS data repository, respectively. Tropospheric column BrO levels were extracted from Theys et al. (2011), Polar oceans and sea-ice regions are global hot spots for the production of biogenic volatile methylated sulfur (VMS) compounds: dimethyl sulfide (DMS) and methanethiol (MeSH). VMS compounds make important contributions to atmospheric particle formation and cloud property modulation, especially when polar atmospheres are pristine. As a result, the polar biogenic sulfur cycle may induce significant climate feedback in response to ongoing sea ice decline. However, polar VMS production, emission, and atmospheric oxidation processes remain poorly represented in current numerical models, hampering assessments of their radiative impacts and, in turn, implementation of targeted observations necessary for providing predictive understanding of changes in the ocean–sea ice–atmosphere (OIA) system. We synthesize current knowledge of the polar biogenic sulfur cycle and its representation in models. To untangle the existing gaps and provide a roadmap toward predictive understanding, we identify key features of sea ice habitats for biological VMS production, sea ice physical features that enhance or suppress VMS emissions, and atmospheric VMS oxidation at low temperatures that controls the contribution of oxidation products to particle formation or growth. These features are tightly coupled, emphasizing the need for coordinated efforts across disciplines that span the OIA interface, and among observational, experimental, and modeling communities. We recommend 4 priority research areas: (1) model representation of biological VMS production at the sea ice bottom and surface; (2) improved quantification of cloud condensation nuclei (CCN) sensitivity to VMS emissions with updated gas phase and multiphase oxidation chemistry at low temperatures; (3) better spatial and seasonal quantification of MeSH abundance and its biological and chemical controls in sea-ice environments; and (4) assessment of the contribution of episodic extreme VMS emissions during sea ice breakup for the polar CCN budget, The authors thank SCOR and the U.S. National Science Foundation through a grant to SCOR (grant no. OCE-2140395).
The authors thank IGAC (NASA grant no. 80NSSC22K0786 for the IGAC International Project Office). SI was funded by JSPS KAKENHI grant no. JP23K28212 and Public Promoting Association Kura Foundation. MDW, CLZ, and KAP were funded by the U.S. National Science Foundation (grant no. AGS-2211153, CHE-2441875, and OPP-2000493). AH was funded by NSERC-NFRF funding for the Horizon2020 project CRiceS (grant no. 101003826). JLT, LM, RL and RP are funded by the Horizon Europe program under Grant 101137680 via the project CERTAINTY and the European Union’s Horizon 2020 program under Grant 101003826 via the project CRiceS. NS was funded by Fisheries and Oceans Canada. RS was funded by the European Research Council (ERC-2018-AdG 834162), the Spanish Ministry of Science and Innovation through the GOOSE grant (PID2022-140872NB-I00), and the “Severo Ochoa Centre of Excellence” accreditation CEX2019-000928-S. IP was funded by Helmholtz Association through the POF-IV program “Changing Earth—Sustaining our Future.” MDM was funded by the Australian Government as part of the Antarctic Science Collaboration Initiative program, under the Australian Antarctic Program Partnership, ASCI000002. HGL was funded by the Korea Institute of Ocean Science and Technology through a project titled “Ocean Circulation and ecosystem variability and predictability research in the earth system model (PEA0175).” SDJ and ASM thank the IITM funding by the Ministry of Earth Sciences, Government of India. GM was funded by the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce (award no. NA23OAR4320198). BD is a research associate at the F.R.S-FNRS, Peer reviewed

Atmospheric aerosol particles act as cloud condensation nuclei (CCN) and play a crucial role in the climate system and hydrological cycle. New particle formation (NPF) events are a significant source of CCN, though their global contribution to the CCN budget remains uncertain. We propose a novel method to estimate the direct contribution of NPF events to CCN concentrations, distinguishing between NPF-generated particles and background or transported aerosols. Previous methods estimated CCN concentration enhancements by comparing CCN concentration at different times periods (during or after NPF events with CCN concentrations prior to the events or with non-event days). Our method overcomes this limitation by focusing on the ceiling size that newly formed particles can reach, the timing of NPF events, and isolating the NPF mode from the overall aerosol size distribution. The method was developed using size-resolved CCN measurements at the Sierra Nevada Station (SNS) in southeastern Spain (2500 m a.s.l.). We demonstrate that the method is also applicable to polydisperse CCN measurements, showing consistency across both variants. Additionally, it has been applied to NPF events at the Izaña Observatory (IZO) in Tenerife (Canary Islands, 2367 m a.s.l.). Both SNS and IZO frequently experience NPF events, though their environmental and aerosol characteristics differ, providing insights into the advantages and limitations of the method. Comparison between the two sites reveals that the mean NPF contribution to CCN is 6.2 ± 4.8 % (IQR = 9 %) at SNS and 24 ± 25 % (IQR = 28 %) at IZO. The higher NPF contribution at IZO is attributed to the ability of newly formed particles to grow to larger sizes and the presence of fewer transported boundary layer particles acting as CCN. Both sites show significant variability in the NPF contribution percentage due to the distinct characteristics of each NPF event., This work was partially supported by NUCLEUS project PID2021-128757OB-I00 funded by MICIU/AEI/10.13039/501100011033/ and “European Union NextGenerationEU/PRTR” and BioCloud project RTI2018.101154.A.I00 funded by MCIN/AEI/10.13039/501100011033 and FEDER “ERDF A way of making Europe”. This work received support from the European Union‘s Horizon 2020 research and innovation program through projects ACTRIS.IMP (grant agreement No 871115) and ATMO_ACCESS (grant agreement No 101008004), by the Spanish Ministry of Science and Innovation through projects ELPIS PID2020-120015RB-I00 MCIN/AEI /10.13039/501100011033 and ACTRIS-España RED2022-134824-E and by University of Granada Plan Propio through Singular Laboratory (LS2022-1) program. The work was also partially funded through EU projects FOCI (101056783), FORCeS (821205), CERTAINTY (101137680) and by Research Council of Finland via Atmosphere and Climate Competence Center (ACCC) Flagship (337549,3570902, 359340) and by University of Helsinki, Faculty of Science via ACTRIS-HY. It was also funded by grants BIOD22_001 and BIOD22_002, funded by Consejería de Universidad, Investigación e Innovación and Gobierno de España and Unión Europea – NextGenerationEU. Andrea Casans thanks Dr. Barbara Bertozzi (from PSI) for her dedication and help with Python programming and Dr. Elisabeth Andrews (from NOAA) for her English revision. Andrea Casans is funded by Spanish ministry of research and innovation under the predoctoral program FPI (PRE2019-090827) funded by MCIN/AEI/ 10.13039/501100011033, FSE “El FSE invierte en tu futuro”. Martin Gysel-Beer contributed to this study as part of ACTRIS-Switzerland funded by the Swiss State Secretariat for Education and Research and Innovation (SERI)., Peer reviewed

The highly variable spatial composition of desert soils remains a major source of uncertainty in estimates of dust radiative forcing. Existing soil mineralogy data sets used in climate models are limited by sparse ground observations and coarse spatial resolution. NASA's Earth surface Mineral dust source InvesTigation (EMIT) addresses this challenge by providing near-global maps of surface mineralogy across dust source regions using imaging spectroscopy at ∼60 m spatial resolution. Here, we evaluate EMIT 60-m mineral mass fraction estimates against ground-based measurements derived from X-ray diffraction and iron wet chemistry analyses of soil samples collected from active dust sources in the Sonoran and Mojave Deserts of Southern California. We also compare particle size distribution measurements with EMIT 60-m soil texture estimates used for estimating mineral mass fractions, including spectrally inactive and thus unretrieved minerals, mainly quartz and feldspar. Both EMIT 60-m mineralogy and soil texture estimates exhibit limited variability compared with ground measurements. Ground measurements reveal a strong Spearman correlation (r<inf>s</inf> = 0.84) between the mass fraction of minerals unretrieved by EMIT and the sand-size mass fraction. We apply empirical adjustments that constrain the unretrieved mineral fraction using sand content and rescale iron oxide mass fractions, significantly improving agreement with ground mineralogy, including an increase in the iron oxide correlation from 0.15 to 0.68. We further develop an empirical method to partition unretrieved minerals into quartz and feldspar. Together, these empirical adjustments provide an efficient approach to enhance EMIT 60-m mineralogy estimates and better represent fine-scale spatial variability in soil mineralogical composition., This work was supported by the NASA Grant 23-EMIT23-0043. We thank Roger N. Clark, Gregory S. Okin, and Francisco Ochoa for helpful discussions and comments. XQ, AGR, and CPG-P acknowledge support from the ERC under the Horizon 2020 research and innovation programme through the FRAGMENT project (grant agreement no. 773051). CPG-P acknowledges support from the Spanish Ministerio de Economía y Competitividad (Grants PID2022-140365OB-I00 and PID2024-157110OI-C21 funded by MCIN/AEI/10.13039/501100011033 and by ERDF/EU), the Horizon Europe programme under Grant Agreement No 101137680 via project CERTAINTY, and the AXA Research Fund through the AXA Chair on Sand and Dust Storms at BSC. We thank Abigail Keebler, Rebecca Greenberger, and Bethany Ehlmann for leading permitting and logistical coordination for soil sample collection in the Mojave Desert (Figures 1c and 2b), conducted as part of a separate project with C.P.G., A.G.R., and X.Q. Selection of campaign region and sampling locations in the Mojave Desert was a collaborative, multi-institutional effort, with site identification and planning led jointly by the Barcelona and Caltech teams and refined during the 2022 field campaign. EMIT-comparable spectra of samples are described in a separate publication led by A. Keebler. Their work was funded by a Caltech Resnick Sustainability Institute Explorer grant to R.G. and B.E. and a NASA FINESST award to A.K. and B.E. (80NSSC22K1456). EMIT is supported by the NASA Earth Venture Instrument program, under the Earth Science Division of the Science Mission Directorate. A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the NASA (80NM0018D0004). Government sponsorship is acknowledged., Peer reviewed

The climate impact of dust is still uncertain, partially due to poorly constrained dust physical and optical properties. Natural dust particles are known to have highly irregular shapes, but many models assume spheres when calculating the direct radiative effect (DRE). While the superior performance of non-spherical shapes in remote sensing applications has been widely recognized, there has been no consensus about the importance of dust non-sphericity in climate models. We assess the extent of the shape effect upon the dust optical properties and DRE at shortwave wavelengths within the NASA Goddard Institute for Space Studies ModelE2.1. We assume tri-axial ellipsoids as an approximation to natural dust shapes that is suitable for model applications, and combine a widely used database of ellipsoidal single-scattering properties with a recent shape distribution constructed from a comprehensive compilation of measurements. We find a shape-induced enhancement of global dust extinction of +20% (+-1%), resulting in a global cooling increase of +24% (+-3%) at the top of atmosphere and +12% (+-2%) at the surface. The ellipsoidal shapes increase the total dust extinction per unit mass, improving our model representation of the dust optical depth compared to a semi-observational constraint and ground-based measurements. However, our analysis shows that the database of ellipsoidal scattering properties covers only one-third of the observed distribution of shapes. This represents a major uncertainty in evaluating the dust shape effect in model applications., Vincenzo Obiso was supported by theNASA Postdoctoral Program at the NASA Goddard Institute for Space Studies, administered by Oak Ridge Associated Universities under contract with NASA. Additional support was provided by theNASA Modeling, Analysis and Prediction Program (NNG14HH42I) and the EarthSurface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures Instrument (EVI‐4) mission, both of which supported Ron L. Miller. Yue Huang was supported by the Columbia University Earth Institute postdoctoral fellowship (2021–2023 Fellow). Carlos Pérez García‐Pando, María Gonçalves Ageitos and Vincenzo Obiso acknowledge funding by the European Research Council (ERC) under the Horizon 2020 research and innovation program through the ERC Consolidator Grant FRAGMENT (Grant agreement no. 773051) and the Spanish Ministerio de Economía y Competitividad through the HEAVY project (Grant PID2022‐140365OB‐I funded by MICIU/AEI/10.13039/501100011033 and byERDF, EU). We additionally acknowledge support from the AXA Research Fund through the AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center (BSC), the European Union's Horizon Europe programme under Grant agreement no. 101137680 via project CERTAINTY, and the Department of Research and Universities of the Government of Catalonia via the Atmospheric Composition Research Group (code 2021 SGR 01550). Jan P. Perlwitz has been funded by NASA Grants 80NSSC19K0056 (Modeling, Analysi sand Prediction Program), 80NSSC19K0984 (Atmospheric Composition Modeling and Analysis Program) and 80NSSC22K1663 (Goddard Institute for Space Studies Model Development)., Peer Reviewed

Aerosol–cloud interactions, particularly ice processes in mixed-phase clouds (MPCs), remain a key source of uncertainty in climate change assessments. This study introduces state-of-the-art laboratory-based parameterizations into a global chemistry–transport model to investigate the contributions of mineral dust (specifically K-feldspar and quartz), marine primary organic aerosol (MPOA), and terrestrial primary biological aerosol particles (PBAPs) to ice-nucleating particles (INPs) in MPCs. The model suggests that INPs originating from PBAPs (INPPBAP) are the primary source of INPs at low altitudes between -10 and -20 °C, particularly in the tropics, with a pronounced peak in the Northern Hemisphere (NH) during the boreal summer. INPPBAP contributes over 40 % of the total simulated INP column burden at midlatitudes. Dust-derived INPs (INPD) are prominent at high altitudes across all seasons, dominating at temperatures below -20 °C, and they constitute over 89 % of the INP average column burden at high latitudes in the NH and about 74 % at high latitudes in the Southern Hemisphere (SH). MPOA-derived INPs (INPMPOA) prevail in the SH at low altitudes, particularly at subpolar and polar latitudes for temperatures above -20 °C, where they represent between 17 % and 36 % of the INP column population, depending on the season. When evaluated against available global observational INP data, the model achieves its highest predictability across all temperature ranges when both INPD and INPMPOA are included as independent INP sources. The addition of INPPBAP does not enhance the model’s ability to reproduce the available observations; however, INPPBAP remains a key contributor to warm-temperature icenucleation events. Therefore, consideration of dust, marine aerosol, and terrestrial bioaerosols as distinct INP species is required to simulate ice nucleation in climate models, This work has been supported by the European Union Horizon 2020 project FORCeS under grant agreement no. 821205. The early stages of this work have been supported by the project “PANhellenic infrastructure for Atmospheric Composition and climatE change” (PANACEA; MIS 5021516), which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund). Nikos Daskalakis, Maria Kanakidou, and Mihalis Vrekoussis were supported by the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy (University Allowance, EXC 2077, University of Bremen). Carlos Pérez García-Pando, Montserrat Costa-Surós, María Gonçalves Ageitos, and Marios Chatziparaschos were supported by the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant no. 773051; FRAGMENT), the Horizon Europe program (Grant Agreement no. 101137680 via project CERTAINTY), and the AXA Research Fund. Montserrat Costa-Surós has received funding from the European Union’s Horizon 2020 research and innovation program, under the Marie Skłodowska-Curie grant agreements, reference 754433 from the call H2020-MSCA-COFUND-2016. Athanasios Nenes was supported by project PyroTRACH (ERC-2016- COG) funded from H2020-EU.1.1. – Excellent Science – European Research Council (ERC), project ID 726165, and the European Union’s Horizon Europe project “CleanCloud” (grant agreement no. 101137639). Stelios Myriokefalitakis, Maria Kanakidou, and Marios Chatziparaschos were supported by the REINFORCE research project implemented in the framework of H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – Next Generation EU (H.F.R.I. project no. 15155).

Although desert dust is the most abundant atmospheric aerosol by mass, its longwave radiative effects remain unclear, obscuring the impacts of dust on weather and climate. Here, using a data-driven analytical model constrained by observations, we show that scattering and absorption of longwave radiation by dust heats the planet by +0.25 ± 0.06 W m⁻² (90% confidence). This is nearly twice the value simulated by current climate models, which omit longwave scattering and underrepresent super coarse dust (diameter > 10 μm). These omissions bias modeled surface energy fluxes, cloud responses, precipitation, and atmospheric circulation. At the global scale, the sign and magnitude of the net dust direct radiative effect remain uncertain, with additional work needed to constrain shortwave cooling effects. These findings show that improving the representation of dust interactions with longwave radiation can improve weather forecasting and is essential to resolve the role of dust in climate change., This work was developed with support from the National Science Foundation (NSF) grants 1856389 and 2151093 awarded to J.F.K. A.K.G. was supported by NSF grant 2151093 during his research tenure at UCLA. We also acknowledge high-performance computing support from NCAR’s Computational and Information Systems Laboratory, sponsored by NSF. A.I. acknowledges MEXT-Program for the advanced studies of climate change projection (SENTAN) grant number JPMXD0722681344. L.L. and N.M.M. acknowledge assistance from the Earth surface Mineral dust source InvesTigation (EMIT), a NASA Earth Ventures-Instrument (EVI-4) Mission, as well as from the Department of Energy (DOE) under award DE-SC0021302. A.A.A. acknowledges support from the U.S. Department of Energy (DOE), Office of Science (award DE-SC0024281). M.K. has received funding from the Helmholtz Association’s Initiative and Networking Fund (grant agreement no. VH-NG-1533). C.P.G.-P. and V.O. acknowledge support from the Spanish Ministerio de Economía y Competitividad through the HEAVY project (grant no. PID2022-140365OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF/EU), the ERC under the Horizon 2020 research and innovation program through the FRAGMENT project (grant agreement no. 773051), the Horizon Europe program under grant agreement no. 101137680 via project CERTAINTY, and the AXA Research Fund through the AXA Chair on Sand and Dust Storms at BSC. M.K. and C.P.G.-P. acknowledge PRACE for granting access to MareNostrum at the Barcelona Supercomputing Center to run MONARCH. R.L.M. received support from the NASA Modeling, Analysis and Prediction Program. P.R.C. and A.R.L received support from the NASA Atmospheric Composition: Modeling and Analysis Program and the NASA Center for Climate Simulation (NCCS) for computational resources., Peer Reviewed, "Article signat per 19 autors/es: Jasper F. Kok, Ashok K. Gupta, Amato T. Evan, Carlos Pérez García-Pando, Longlei Li, Adeyemi A. Adebiyi, Samuel Albani, Yves Balkanski, Ramiro Checa-Garcia, Peter R. Colarco, Douglas S. Hamilton, Yue Huang, Akinori Ito, Martina Klose, Natalie M. Mahowald, Ron L. Miller, Vincenzo Obiso, Adriana Rocha Lima & Jessica Wan "

Atmospheric deposition of micro-nutrients like Fe has been shown to be important for ocean biogeochemistry. The largest source of atmospheric Fe and other elements (e.g., Ca, Al, Si, and Ti) is desert dust, although there are significant non-dust sources in some regions (e.g., combustion, sea salts, volcanoes). However, past estimates of these elements have been substantially uncertain due to limited information about the composition of the desert source regions. Here we use elemental distributions estimated from new Earth Surface Mineral Dust Source Investigation (EMIT) observations, which provide mineralogical composition at the surface of the Earth based on imaging spectroscopy measurements from the International Space Station. We focus on total elemental amounts, not on the soluble fraction. We add in other sources of these elements (anthropogenic and natural) and compare to a compilation of available surface concentration data from stations over land and from shipborne observations. The combined observational and model synthesis provides new information about the distribution and deposition of these elements. Our results suggest that the modeled distribution is similar to available observations, but discrepancies still exist in both natural desert dust regions as well as regions dominated by anthropogenic sources. Comparisons between the model estimated Ca/Al ratios and observations in some dust dominated regions suggest an underestimate of Ca/Al ratios. Global budgets for Ca, Al, Fe, Si, and Ti suggest that desert dust remains the dominant source, although volcanic and anthropogenic contributions are important in some regions. Changes in elemental distributions since preindustrial times were also estimated., NMM and LL would like to acknowledge the support of NASA Grant 80NSSC20K1674 and the EMIT project. EMIT is supported by the NASA Earth Venture Instrument program under the Earth Science Division of the Science Mission Directorate. NMM and LL would also like to acknowledge the support of the high-performance computing resources from Derecho provided by National Center for Atmospheric Research (NCAR)'s Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation (NSF). We would also like to acknowledge the assistance of Paul Ekhart (EBAS), the many freely available air quality websites acknowledged in the paper: EBAS (https://ebas.nilu.no/)--including data affiliated with ACTRIS (Aerosol Clouds and Trace gas Research Infrastructure), EMEP (European Monitoring and Evaluation Programme), GAW-WDCA (Global Atmosphere Watch-World Data Centre for Aerosols), EANET Acid Deposition Monitoring Network in (East Asian)--All Indian Air Quality Management data (https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing/data), US EPA CSN and IMPROVE, INDAAF ACTRIS-Fr (International Network to study Deposition and Atmospheric chemistry in Africa) (http://indaaf.obs-mip.fr). DDC would like to acknowledge the National Collaborative Research Infrastructure Scheme (NCRIS) for financial support. A portion of the research related to EMIT mission data products was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). SC is grateful to the Gulf Research Program under the U.S. National Academies of Sciences, Engineering, and Medicine (Award No. SCON-10000653) for financial support. JPE acknowledges the support by the Department of Defence, of the Enhanced Particulate Matter Surveillance Program (EPMSP), contract number: W9124R-05-C-0135/SUBCLIN 000101-ACRN-AB, specifically for the aerosol data from the Middle East. M.G.A and C.P.G.P. acknowledge support from the Spanish Ministerio de Economía y Competitividad through the HEAVY and DUSTIMPACT project (Grants PID2022-140365OB-I00 and PID2024-157110OI-C21) and the BIOTA project (PID2022-139362OB-I00) funded by MCIN/AEI/10.13039/501100011033 and by ERDF/EU, the ERC under the Horizon 2020 research and innovation programme through the FRAGMENT project (Grant agreement no. 773051), the Horizon Europe programme under Grant Agreement No 101137680 via project CERTAINTY, and the AXA Research Fund through the AXA Chair on Sand and Dust Storms at BSC. RLM was supported by the NASA Earth Mineral Dust Source Investigation (EMIT) and the NASA Modeling, Analysis and Prediction Program. PA acknowledge FAPESP - Fundação de Amparo à Pesquisa do Estado de São Paulo, Grant 2023/04358-9. FL acknowledges Chile's Sistema de Información Nacional de Calidad del Aire (https://sinca.mma.gob.cl/index.php/). H.M. was supported by the MEXT/JSPS KAKENHI Grants (JP23H00515, JP23K18519, JP23K24976, JP24H02225, JP26K00780, and JP26K03065), the MEXT Arctic Challenge for Sustainability II (ArCS II; JPMXD1420318865) and 3 (ArCS-3; JPMXD1720251001) Projects, and the Environment Research and Technology Development Fund 2–2301 (JPMEERF20232001) and 2–2602 (JPMEERF20262002) of the Environmental Restoration and Conservation Agency. SR acknowledge the support of the project AERO-EXTREME (PID2021-125669NB-I00) funded by the State Research Agency/Agencia Estatal de Investigación of Spain and the European Regional Development Funds. GS and RUS acknowledge the GEOVIDE grant (ANR-13-B506-0014). The work of the University of Aveiro was partially supported by national funds through FCT – Fundação para a Ciência e a Tecnologia I.P., under the project CESAM-Centro de Estudos do Ambiente e do Mar, references UID/50017/2025 (https://doi.org/10.54499/UID/50017/2025) and LA/P/0094/2020 (https://doi.org/10.54499/LA/P/0094/2020). CSB acknowledges National Science Foundation support of GEOTRACES projects under awards OCE-1454368, OCE-1438047, and OCE-1756103. The Italian scientific activities at THAAO are partially founded by INGV and ENEA through support agreements with the National Science Foundation (USA) and Inussuk (DK). Long-term PM 10 measurements at Lampedusa have been carried out jointly by ENEA and University of Florence within several research projects funded by the Italian Ministry for University and Research and the Ministry of the Environment. Contributions at Lampedusa by Damiano Sferlazzo are gratefully acknowledged., Peer Reviewed, Article signat per 62 autors/es: Natalie M. Mahowald 1, Longlei Li1, María Gonçalves Ageitos 2,3, Ronald L. Miller 4, Carlos Pérez García‐Pando 2,5, Paul Ginoux 6, Vincenzo Obiso 2, Philip G. Brodrick 7, Roger N. Clark 8, Karine Desboeufs 9, Robert O. Green 7, Gregory S. Okin 10, David R. Thompson 7, Sagar Rathod 11, Esteban Gazel 1, Adrian Hornby 1, Emile Journet 12, Maria Grazia Alaimo 13, Célia Alves 14, Andres Alastuey 15, Paulo Artaxo 16, Alex R. Baker 17, Francisco Barraza 18, Silvia Becagli 19, Clifton S. Buck 20, Giulia Calzolai 21, Shankararaman Chellam 22, Ying Chen 23, Patrick Y. Chuang 24, David D. Cohen 25, Cristina Colombi 26, Evangelia Diapouli 27, Gaetano Dongarra 13, Konstantinos Eleftheriadis 27, Johann P. Engelbrecht 28, Corinne Galy‐Lacaux 29, Cassandra Gaston 30, Dario Gomez 31, Yenny González Ramos 32,33, Jenny Hand 34, Roy M. Harrison 35, Barak Herut 36,37, Philip K. Hopke 38,39, Zsofia Kertesz 40, Katriina Kyllönen 41, Fabrice Lambert 42,43, Willam M. Landing 44, Remi Losno 45, Franco Lucarelli 21,46, Willy Maenhaut 47, Christopher Marsay 48, Hitoshi Matsui 49,50, Randall V. Martin 51, Yasser Morera‐Gómez 52,Adina Paytan 53, Joseph Prospero 30, Sergio Rodríguez 32,54, Geraldine Sarthou 55, Rachel Shelley 17, Ronald L. Siefert 56, Patricia Smichowski 31, and Daniela Varrica 13 / 1 Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA, 2 Barcelona Supercomputing Center, Barcelona, Spain, 3 Department of Project and Construction Engineering, Universitat Politècnica de Catalunya –BarcelonaTech, Terrassa, Spain, 4 NASA Goddard Institute for Space Studies, New York, NY, USA, 5 ICREA, Catalan Institution for Research and Advances Studies, Barcelona, Spain, 6 NOAA/OAR Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA, 7 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, 8 Planetary Science Institute, Tucson, AZ, USA, 9 CNRS, LISA, Université Paris Cité and Université Paris Est Creteil, Paris, France,10 University of California Los Angeles, Los Angeles, CA, USA, 11 La Follette School of Public Affairs, University of Wisconsin‐Madison, Madison, WI, USA, 12 CNRS, LISA, Université Paris Est Creteil and Université Paris Cité, Créteil, France, 13 Dipartimento di Scienze della Terra e del Mare, University of Palermo, Palermo, Italy, 14 Department ofEnvironment, Centre for Environmental and Marine Studies (CESAM), University of Aveiro, Aveiro, Portugal, 15 Institute of Environmental Assessment and Water Research (IDAEA‐CSIC), Barcelona, Spain, 16 Instituto de Fisica, Universidade de Sao Paulo, Sao Paulo, Brazil, 17 School of Environmental Sciences, University of East Anglia, Norwich, UK, 18 Saw Science, Invercargill, New Zealand, 19 Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino,Italy, 20 Skidaway Institute of Oceanography, University of Georgia, Savannah, GA, USA, 21 National Institute for Nuclear Physics (INFN) ‐ Florence Division, Sesto Fiorentino, Italy, 22 Department of Civil & Environmental Engineering and Department of Chemical Engineering, Texas A&M University, College Station, TX, USA, 23 Department of Environmental Science and Engineering, Fudan University, Shanghai, China, 24 Earth & Planetary Sciences Department, University of California Santa Cruz, Santa Cruz, CA, USA, 25 Centre for Accelerator Science, Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW, Australia, 26 Environmental Monitoring Sector, Arpa Lombardia, Milan, Italy, 27 Environmental Radioactivity & Aerosol Technology for Atmospheric & Climate Impact Lab, INRaSTES, N.C.S.R.Demokritos, Attiki, Greece, 28 Desert Research Institute (DRI), Reno, NV, USA, 29 CNRS, IRD, LAERO, University of Toulouse, Toulouse, France, 30 Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, FL, USA, 31 Comision Nacional de Energia Atomica, Gerencia Química, Buenos Aires, Argentina, 32 Izaña Atmospheric Research Center (IARC), Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, Spain, 33 Scientific Department, CIMEL, Paris, France, 34 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA, 35 School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK, 36 Israel Oceanographic & Limnological Research, Haifa, Israel, 37 Faculty of Marine Sciences, Ruppin Academic Center, Michmoret, Israel, 38 Institute for a Sustainable Environment, Clarkson University, Potsdam, NY, USA ,39 Department of Environmental Medicine and Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA, 40 HUN‐REN Institute for Nuclear Research (ATOMKI), Debrecen, Hungary, 41 Finnish Meteorological Institute, Helsinki, Finland, 42 Geography Institute, Pontificia Universidad Catolica de Chile, Santiago, Chile, 43 Center for Climate and Resilience Research, Santiago, Chile, 44 Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA, 45 Institut de Physique du Globe de Paris, CNRS, Université Paris Cité, Paris, France, 46 Physics and Astronomy Department, University of Florence, Sesto Fiorentino, Italy, 47 Department of Chemistry, Ghent University, Gent, Belgium, 48 School of Marine Science and Policy, University of Delaware, Newark, DE,USA, 49 Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan, 50 Institute for Space–Earth Environmental Research, Nagoya University, Nagoya, Japan, 51 Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA, 52 Universidad de Navarra, Instituto de Biodiversidad y Medioambiente BIOMA, Pamplona, Spain, 53 Earth and Planetary Science, University of California, Santa Cruz, CA, USA, 54 Consejo Superior de Investigaciones Científicas, IPNA CSIC, Tenerife, Spain, 55 CNRS, IRD, Ifremer, LEMAR, University of Brest, Plouzané, France, 56 Chemistry Department, United States Naval Academy, Annapolis, MD, USA, 13 - Acció per al Clima

Aerosol typing plays an important role in identifying aerosol sources, monitoring environmental events,
and mitigating impacts. Understanding the origin and characteristics of aerosols is essential to detect aerosol
hazards, issue early warnings and minimize their potential impact on climate, air quality, and public health.
To date, several aerosol typing schemes have been developed within ACTRIS (Aerosol, Clouds and Trace
Gases Research Infrastructure) and EARLINET (European Aerosol Research Lidar Network;
data.earlinet.org) and provide the predominant aerosol type or aerosol mixture in the atmospheric column
[1-5]. Similarly, aerosol classification from space lidars offers global coverage and can either be used
complementary in retrievals or radiative-closure assessments., This research was financially supported by the PANGEA4CalVal project (Grant Agreement 101079201) funded by the European Union, and the CERTAINTY project (Grant Agreement 101137680) funded by the Horizon Europe program. The authors also acknowledge ITINERIS, Italian Integrated Environmental Research Infrastructure System (IR0000032, D.D. n. 130/2022—CUPB53C22002150006). We acknowledge EARLINET and ACTRIS aerosol remote sensing teams for the provision of datasets, and for scientific support., Peer Reviewed

Mineral dust is sourced from arid regions around the globe, and its composition impacts regional and global environments by affecting radiation balance, cloud formation, nutrient deposition, and snowmelt. As such, mineral dust plays a key role in Earth's energy and biogeochemical budgets. Global remote sensing of dust source regions have the potential to significantly improve our understanding of mineral dust composition and its environmental impacts. While, visible to shortwave infrared data in remote measurements effectively map surface mineralogy for large swaths of mineral groups, they struggle to detect quartz and feldspar, the two most abundant minerals on Earth's surface. Broadband thermal infrared remotely sensed data are capable of remote measurements of quartz and feldspar, but on their own are limited in their ability to resolve confounding factors including other minerals and vegetation. Here, we demonstrate the utility of joint visible to thermal infrared (VTIR) data by leveraging existing remote sensing data products and spectral libraries in a Monte Carlo spectral mixing analysis to remotely retrieve global quartz and feldspar areal distributions with grain size information. Resulting latitudinal and regional-scale distributions are consistent with expectations based on geological settings. Using a case study, we demonstrate our VTIR-based unmixing strategy is theoretically capable of further separating grain sizes and chemistries. These results highlight the utility of VTIR data, the value in further collection of these data as part of future space-based Earth observations, and the need for improved VTIR spectral libraries for interpretation., We would like to acknowledge the contributions of the entire EMIT engineering and science teams and the ISS team for enabling the EMIT mission. A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). C.P.G.P and M.G.A acknowledge support from the Spanish Ministerio de Economía y Competitividad through the HEAVY project (Grant PID2022-140365OB-I00) and the BIOTA project (PID2022-139362OB-I00) both funded by MCIN/AEI/10.13039/501100011033 and by ERDF/EU, the ERC under the Horizon 2020 research and innovation programme through the FRAGMENT project (grant agreement no. 773051), the Horizon Europe programme under Grant agreement no. 101137680 via project CERTAINTY, and the AXA Research Fund through the AXA Chair on Sand and Dust Storms at BSC., Peer Reviewed, 13 - Acció per al Clima

The climate impact of dust is still uncertain, partially due to poorly constrained dust physical and optical properties. Natural dust particles are known to have highly irregular shapes, but many models assume spheres when calculating the direct radiative effect (DRE). While the superior performance of non-spherical shapes in remote sensing applications has been widely recognized, there has been no consensus about the importance of dust non-sphericity in climate models. We assess the extent of the shape effect upon the dust optical properties and DRE at shortwave wavelengths within the NASA Goddard Institute for Space Studies ModelE2.1. We assume tri-axial ellipsoids as an approximation to natural dust shapes that is suitable for model applications, and combine a widely used database of ellipsoidal single-scattering properties with a recent shape distribution constructed from a comprehensive compilation of measurements. We find a shape-induced enhancement of global dust extinction of +20% (+-1%), resulting in a global cooling increase of +24% (+-3%) at the top of atmosphere and +12% (+-2%) at the surface. The ellipsoidal shapes increase the total dust extinction per unit mass, improving our model representation of the dust optical depth compared to a semi-observational constraint and ground-based measurements. However, our analysis shows that the database of ellipsoidal scattering properties covers only one-third of the observed distribution of shapes. This represents a major uncertainty in evaluating the dust shape effect in model applications., Vincenzo Obiso was supported by theNASA Postdoctoral Program at the NASA Goddard Institute for Space Studies, administered by Oak Ridge Associated Universities under contract with NASA. Additional support was provided by theNASA Modeling, Analysis and Prediction Program (NNG14HH42I) and the EarthSurface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures Instrument (EVI‐4) mission, both of which supported Ron L. Miller. Yue Huang was supported by the Columbia University Earth Institute postdoctoral fellowship (2021–2023 Fellow). Carlos Pérez García‐Pando, María Gonçalves Ageitos and Vincenzo Obiso acknowledge funding by the European Research Council (ERC) under the Horizon 2020 research and innovation program through the ERC Consolidator Grant FRAGMENT (Grant agreement no. 773051) and the Spanish Ministerio de Economía y Competitividad through the HEAVY project (Grant PID2022‐140365OB‐I funded by MICIU/AEI/10.13039/501100011033 and byERDF, EU). We additionally acknowledge support from the AXA Research Fund through the AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center (BSC), the European Union's Horizon Europe programme under Grant agreement no. 101137680 via project CERTAINTY, and the Department of Research and Universities of the Government of Catalonia via the Atmospheric Composition Research Group (code 2021 SGR 01550). Jan P. Perlwitz has been funded by NASA Grants 80NSSC19K0056 (Modeling, Analysi sand Prediction Program), 80NSSC19K0984 (Atmospheric Composition Modeling and Analysis Program) and 80NSSC22K1663 (Goddard Institute for Space Studies Model Development)., Peer Reviewed, Postprint (published version)

Although desert dust is the most abundant atmospheric aerosol by mass, its longwave radiative effects remain unclear, obscuring the impacts of dust on weather and climate. Here, using a data-driven analytical model constrained by observations, we show that scattering and absorption of longwave radiation by dust heats the planet by +0.25 ± 0.06 W m⁻² (90% confidence). This is nearly twice the value simulated by current climate models, which omit longwave scattering and underrepresent super coarse dust (diameter > 10 μm). These omissions bias modeled surface energy fluxes, cloud responses, precipitation, and atmospheric circulation. At the global scale, the sign and magnitude of the net dust direct radiative effect remain uncertain, with additional work needed to constrain shortwave cooling effects. These findings show that improving the representation of dust interactions with longwave radiation can improve weather forecasting and is essential to resolve the role of dust in climate change., This work was developed with support from the National Science Foundation (NSF) grants 1856389 and 2151093 awarded to J.F.K. A.K.G. was supported by NSF grant 2151093 during his research tenure at UCLA. We also acknowledge high-performance computing support from NCAR’s Computational and Information Systems Laboratory, sponsored by NSF. A.I. acknowledges MEXT-Program for the advanced studies of climate change projection (SENTAN) grant number JPMXD0722681344. L.L. and N.M.M. acknowledge assistance from the Earth surface Mineral dust source InvesTigation (EMIT), a NASA Earth Ventures-Instrument (EVI-4) Mission, as well as from the Department of Energy (DOE) under award DE-SC0021302. A.A.A. acknowledges support from the U.S. Department of Energy (DOE), Office of Science (award DE-SC0024281). M.K. has received funding from the Helmholtz Association’s Initiative and Networking Fund (grant agreement no. VH-NG-1533). C.P.G.-P. and V.O. acknowledge support from the Spanish Ministerio de Economía y Competitividad through the HEAVY project (grant no. PID2022-140365OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF/EU), the ERC under the Horizon 2020 research and innovation program through the FRAGMENT project (grant agreement no. 773051), the Horizon Europe program under grant agreement no. 101137680 via project CERTAINTY, and the AXA Research Fund through the AXA Chair on Sand and Dust Storms at BSC. M.K. and C.P.G.-P. acknowledge PRACE for granting access to MareNostrum at the Barcelona Supercomputing Center to run MONARCH. R.L.M. received support from the NASA Modeling, Analysis and Prediction Program. P.R.C. and A.R.L received support from the NASA Atmospheric Composition: Modeling and Analysis Program and the NASA Center for Climate Simulation (NCCS) for computational resources., Peer Reviewed, "Article signat per 19 autors/es: Jasper F. Kok, Ashok K. Gupta, Amato T. Evan, Carlos Pérez García-Pando, Longlei Li, Adeyemi A. Adebiyi, Samuel Albani, Yves Balkanski, Ramiro Checa-Garcia, Peter R. Colarco, Douglas S. Hamilton, Yue Huang, Akinori Ito, Martina Klose, Natalie M. Mahowald, Ron L. Miller, Vincenzo Obiso, Adriana Rocha Lima & Jessica Wan "

Aerosol typing plays an important role in identifying aerosol sources, monitoring environmental events,
and mitigating impacts. Understanding the origin and characteristics of aerosols is essential to detect aerosol
hazards, issue early warnings and minimize their potential impact on climate, air quality, and public health.
To date, several aerosol typing schemes have been developed within ACTRIS (Aerosol, Clouds and Trace
Gases Research Infrastructure) and EARLINET (European Aerosol Research Lidar Network;
data.earlinet.org) and provide the predominant aerosol type or aerosol mixture in the atmospheric column
[1-5]. Similarly, aerosol classification from space lidars offers global coverage and can either be used
complementary in retrievals or radiative-closure assessments., This research was financially supported by the PANGEA4CalVal project (Grant Agreement 101079201) funded by the European Union, and the CERTAINTY project (Grant Agreement 101137680) funded by the Horizon Europe program. The authors also acknowledge ITINERIS, Italian Integrated Environmental Research Infrastructure System (IR0000032, D.D. n. 130/2022—CUPB53C22002150006). We acknowledge EARLINET and ACTRIS aerosol remote sensing teams for the provision of datasets, and for scientific support., Peer Reviewed, Postprint (published version)

Aerosol–cloud interactions, particularly ice processes in mixed-phase clouds (MPCs), remain a key source of uncertainty in climate change assessments. This study introduces state-of-the-art laboratory-based parameterizations into a global chemistry–transport model to investigate the contributions of mineral dust (specifically K-feldspar and quartz), marine primary organic aerosol (MPOA), and terrestrial primary biological aerosol particles (PBAPs) to ice-nucleating particles (INPs) in MPCs. The model suggests that INPs originating from PBAPs (INPPBAP) are the primary source of INPs at low altitudes between -10 and -20 °C, particularly in the tropics, with a pronounced peak in the Northern Hemisphere (NH) during the boreal summer. INPPBAP contributes over 40 % of the total simulated INP column burden at midlatitudes. Dust-derived INPs (INPD) are prominent at high altitudes across all seasons, dominating at temperatures below -20 °C, and they constitute over 89 % of the INP average column burden at high latitudes in the NH and about 74 % at high latitudes in the Southern Hemisphere (SH). MPOA-derived INPs (INPMPOA) prevail in the SH at low altitudes, particularly at subpolar and polar latitudes for temperatures above -20 °C, where they represent between 17 % and 36 % of the INP column population, depending on the season. When evaluated against available global observational INP data, the model achieves its highest predictability across all temperature ranges when both INPD and INPMPOA are included as independent INP sources. The addition of INPPBAP does not enhance the model’s ability to reproduce the available observations; however, INPPBAP remains a key contributor to warm-temperature icenucleation events. Therefore, consideration of dust, marine aerosol, and terrestrial bioaerosols as distinct INP species is required to simulate ice nucleation in climate models, This work has been supported by the European Union Horizon 2020 project FORCeS under grant agreement no. 821205. The early stages of this work have been supported by the project “PANhellenic infrastructure for Atmospheric Composition and climatE change” (PANACEA; MIS 5021516), which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund). Nikos Daskalakis, Maria Kanakidou, and Mihalis Vrekoussis were supported by the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy (University Allowance, EXC 2077, University of Bremen). Carlos Pérez García-Pando, Montserrat Costa-Surós, María Gonçalves Ageitos, and Marios Chatziparaschos were supported by the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant no. 773051; FRAGMENT), the Horizon Europe program (Grant Agreement no. 101137680 via project CERTAINTY), and the AXA Research Fund. Montserrat Costa-Surós has received funding from the European Union’s Horizon 2020 research and innovation program, under the Marie Skłodowska-Curie grant agreements, reference 754433 from the call H2020-MSCA-COFUND-2016. Athanasios Nenes was supported by project PyroTRACH (ERC-2016- COG) funded from H2020-EU.1.1. – Excellent Science – European Research Council (ERC), project ID 726165, and the European Union’s Horizon Europe project “CleanCloud” (grant agreement no. 101137639). Stelios Myriokefalitakis, Maria Kanakidou, and Marios Chatziparaschos were supported by the REINFORCE research project implemented in the framework of H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – Next Generation EU (H.F.R.I. project no. 15155)., Postprint (published version)